The Fit Factor:
Matching Loans and Savings to Cash Flows

Yoshinari Noguchi
yoshinari.noguchi@gojo.co
Cheriel Neo
cheriel.neo@gojo.co
August 2021

With thanks to Stuart Rutherford for his advice and contributions
and to L-IFT for their funding of the Hrishipara Daily Diaries projectIntroduction
In ﬁnancial inclusion, impact measurement tends to focus on what happens after ﬁnancial services
are provided. For example, whether sales, income, or employment increased after a loan.
However, these outcomes are usually not the result of the ﬁnancial services alone. An inconvenientloan, a high-fee or high-interest loan, or even a predatory loan, can produce good results if thebusiness or the environment is right. Conversely, someone using a client-friendly ﬂexible loan or a
low-interest loan may meet with an unavoidable accident, disaster, or depression. Figure 1 sets out
the interaction between the loan product and the outcomes of the loan. Further, unlucky events
may happen to many borrowers simultaneously, making it di cult to separate the effects of theloan from the suitability of the loan.

Instead of assessing the impact of a ﬁnancial service by looking at the outcomes that follow its
delivery, what if we look at how well the service suited the customer at the time it was delivered?
We call this the ‘Fit’ between a ﬁnancial service and a customer’s cash ﬂows, and introduce thefollowing deﬁnition of ﬁtness:
Volatility(Household Cash Flow Without the Financial Service) Volatility(Household Cash Flow With the Financial Service)

1The deﬁnition is quantitative to allow for cross-sectional comparison across households andacross ﬁnancial services and/or providers. It is the volatility of household cash ﬂow without theﬁnancial service, minus the volatility of household cash ﬂow with the ﬁnancial service, withvolatility evaluated by standard deviation. It can also be explained as subtracting the volatility of
the actual total cash ﬂow from the volatility of the hypothetical cash ﬂow "if the ﬁnancial service
had not been delivered?". If the difference between the two volatilities is positive and large, theservice is interpreted as providing Fit. If the difference is negative, it is interpreted as causing
additional volatility.
We know from the work of Stuart Rutherford and other ﬁnancial diary researchers in Portfolios ofthe Poor that a key function of ﬁnancial services for low-income people is enabling them to manage
their money better, by moving money through space and/or time. Volatile net cash ﬂows require
more active management and can therefore be inconvenient and time-consuming for low-income
people. The Fit aims to measure the extent to which a ﬁnancial service contributes to easier money
management by reducing volatility in net cash ﬂows.
Take the case (Figure 2) where a large investment is made in equipment which then generates aseries of positive cash ﬂows:

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If a loan were to be taken to pay part of the initial investment (Figure 3), with repayments spreadequally over the following months, volatility is reduced by a large margin. In other words, it provides
a positive Fit.

In the context of microﬁnance, the cash ﬂow for the customer's business is often inseparable fromthe cash ﬂow for the customer's life. In real life, most microﬁnance customers’ net cash ﬂows do
not stay the same from month to month, but tend to be uneven and irregular (Figure 4).

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Often, loans are not only used for investment, but also to support a variety of life’s ﬁnancial needs.
In such cases where a customer may parcel out the money received from the loan over several
smaller expenditures over time, we may not see a neat ﬁt between the timing of their actual cash
ﬂows and the loan schedule (Figure 5).

The Fit could be improved if the timing of the loan matched a big expenditure. In Figure 6 we showwhat it would look like if the loan was taken in August, when a large expenditure was made.

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Providing the loan in the form of a credit line contract (as in Figure 7) would result in an even greater
Fit improvement:

Holding a large sum of money from a loan (as in Figure 5) may be inconvenient or less than ideal fora microﬁnance customer, especially if they do not have a bank account. While the money sits idle intheir home or bank account, they begin paying interest on the full sum before they are able to put it
to work for them. In addition, the loan schedule may actually increase the volatility of their monthly
cash ﬂows, making it more di cult for them to plan and manage their money.
If the microﬁnance customer were able to match the timing and amount of their loan(s) moreclosely to their actual cash outﬂows (as in Figures 6 and 7), their monthly repayment amounts might
be smaller, and they would not have to deal with large sums of loan money sitting idle on theirhands.

Real Life Examples
Moving from hypothetical examples, we next review some actual transactions to examine thevalidity of the Fit Factor in real life. We also discuss actions that ﬁnancial institutions could take toimprove their ﬁnancial services from the Fit Factor perspective. The analysis was conducted using
detailed transaction data from the Hrishipara Financial Diaries.

5Figure 8 shows the monthly ﬁnancial transactions of one of the Hrishipara diarists with a large MFI(in gold) and all the diarist’s other transactions (in teal). In both cases these are net transaction
values. In other words, the gold bars show the balance of incoming and outgoing ﬁnancialtransactions with the particular MFI, and the teal bars show the balance of incoming and outgoingtransactions of all other kinds – including business cash ﬂows, family cash ﬂows, and, possibly,transactions with other MFIs or ﬁnancial partners. Figure 8 is an example of a ﬁnancial institution
providing a very close Fit, using a combination of loans and savings.

The standard deviation of the sum of the teal and the gold bars is minimal here, compared to thestandard deviation of the teal bars alone. The gold and teal bars are almost symmetrical, as if
mirrored across the zero line.
The positive teal bars in May and October 2018 are mostly from land sales. In these months, theMFI’s savings service absorbed the incoming cash, as seen in the corresponding negative gold bars.
The cost and risk of storing cash in hand were avoided. In February 2018 and January and February
2019, the diarist took loans (positive gold bars) and lent most of them to brother and sister (negative6teal bars). The close Fit happened because the MFI both absorbed surplus funds and supplied funds
when needed.
In the next example (Figure 9) no particular Fit occurred. The diarist uses the same large MFI as theprevious diarist, but does not take loans and mainly uses its savings services.

The diarist deposited the same amount every week, regardless of income or expenditure. Adding
this constant to each element of the series did not affect the standard deviation, so the Fit isalmost zero. But it is not a negative Fit. Since no withdrawals are observed, we infer that the ﬂow ofdeposits contributed to the growth of the savings balance. This suggests that Fit may not be thebest measure for evaluating the impact of this savings service. The diarist may also experience theregularity of the savings as a useful discipline in their money management practices.
The following (Figure 10) is an example of a negative Fit. The diarist uses the same large MFI as in
the previous cases, with a combination of loans and savings. In 2017, 2018, and 2019, loans taken in
November are large compared to other cash ﬂows. In 2017 and 2019, the sum of other cash ﬂows ispositive, so it is unlikely that the diarist would have experienced trouble if they hadn’t taken theloans.

7However, the utility of a loan does not necessarily stem solely from the immediate cash ﬂow. For
example, some borrowers may like to have cash on hand to respond to any sudden shortage offunds. This diarist had a big expenditure in January 2018 (a loan to a daughter), and a large part of
the funds probably came from the loan taken in November.

Nevertheless, from the ﬁnancial institution's perspective, there is room for improvement. For
example, if the timing of loans had been more ﬂexible, the loan disbursed in November 2017 could
have been delayed to January 2018, and the diarist would have avoided the cost and risk of keeping
cash in hand for two months. Providing such ﬂexibility would have improved the Fit.

Limitations
One limitation is that our indicator may appear tautological, since we deﬁne our own measure ofﬁtness and then judge MFI performance by it. It requires that when a customer makes a largetransaction, the ﬁnancial institution simultaneously provides a ﬁnancial counter-transaction. Butin real life, large transactions are usually ones that the customer wants to make or cannot avoid
making, and the timing of such transactions are often not easy to change. If so, the key to providing

8Fit for ﬁnancial institutions is to offer ﬂexible timings for disbursing loans, accepting repayments,
and depositing and withdrawing savings.
Of course, even where there are matching transactions and our indicator evaluates it as Fit, it is not
self-evident whether it was the actions of the customer or of the ﬁnancial institution that caused
the Fit. For example, if a customer delayed a capital investment by waiting for a loan to be provided,
one might conclude that the customer played the bigger role in creating the Fit. Nevertheless, it is
not usually easy to distinguish the contributions made by each of the two parties.
As long as the customer is willing to wait for the loan to make their expenditure, a ﬁnancial
institution is able to provide Fit. However, in an environment where the customer has many loan
providers to choose from, they may be less willing to wait for one speciﬁc ﬁnancial institution todisburse a loan. Therefore, in a competitive market, ﬁnancial institutions must focus on fast andﬂexible services in order to ensure they have the opportunity to provide Fit to their clients. Whenapplied to a competitive market, the Fit indicator measures the speed and ﬂexibility of a particularprovider’s services.

Comparing providers
One of the beneﬁts of using quantitative indicators is comparability. Even if they are less accuratein evaluating a single transaction, their utility in evaluating and comparing multiple transactions
using the same criteria is signiﬁcant. Here we compare two ﬁnancial institutions that provided
ﬁnancial services to the diarists participating in the Hrishipara Financial Diaries in terms of the Fitindicator.
The Hrishipara Financial Diaries has been running since 2015, and about 60 diarists are
participating in it. A large MFI, with many clients in Hrishipara and the surrounding area, provides
ﬁnancial services to 33 of the diarists (counting only those who have been transacting for morethan 12 months). Another provider, a co-op, is a much smaller ﬁnancial institution that provides
ﬁnancial services to 35 of the diarists (again counting only those who have been transacting for
more than 12 months). Eighteen diarists have transactions at both of these ﬁnancial institutions.

9For this comparison, we have standardized the Fit indicator by dividing it by its ﬁrst term toevaluate the difference in volatility relative to the size of each person’s cash ﬂows. Until now wehave expressed the Fit, as the difference in volatilities, in dollar terms. For a poor borrower withrelatively small cash ﬂows, however, even if the Fit is small in dollar terms, the impact on theirﬁnances may be signiﬁcant if the difference is signiﬁcant in percentage terms. The following
standardisation of the Fit therefore helps us better understand the impact on each individual’s cash
ﬂows.
(Volatility(Household Cash Flow Without the Financial Service) Volatility(Household Cash Flow With the Financial Service))
Volatility(Household Cash Flow Without the Financial Service)
Figure 11a shows the Fit evaluation for all diarists who used the services of the MFI, and Figure 11bshows that of diarists who used the co-op.

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Each bar represents one customer experience, and a positive bar represents a good Fit andnegative one a bad Fit. Note that a bar near zero does not imply a poor ﬁt. There are cases wherethe impact can be evaluated from a different perspective than Fit, such as regular ﬁxed amountdeposit services.
In the large MFI (Figure 11a), out of 33 diarists, 22 were positive Fit, and 11 were negative Fit. They
are providing excellent Fits to many customers but there were several cases where the Fit wasnegative. Looking at the details of the excellent Fits, we found that the combination of loans andsavings was successful in several cases, as shown in a previous example (Figure 8). In the negative
Fits, there were several cases where loans were taken but not spent. In some of these cases, theloan o cer is reported to have pushed loans that the customer did not particularly want, probably
for the sake of the loan o cer's performance. The dark bars indicate the examples used in theprevious sections.
In the small co-op (Figure 11b), out of 35 diarists, 25 were positive Fit, and 10 were negative Fit. They
are providing excellent Fits to multiple customers, though there are some negative Fits. Looking at
the excellent Fit details, we found several cases where the savings service provided ﬂexibility,
allowing diarists to deposit as and when they had large cash inﬂows. Also, ﬂexibility was observed

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in the timing of loans and repayments, where diarists were permitted to repay what they could,
whenever they could. There were some cases in the negative Fits where our periodization createdthe illusion of a negative Fit, which will be discussed in the next section. There were also somecases where it was assumed that the data did not fully capture the transaction.
The Fit for the small co-op was 11.6% on average, higher than the 9.2% of the large MFI. In terms ofstandard deviation of the Fit, the small co-op provided relative consistency at 27.3%, lower thanthe large MFI's 30.0%. The thickness of the negative Fit wing in the large MFI may contribute to thisdifference.

NoiseIn the example analysis above, we have aggregated the daily diary data into monthly data for 1) theﬁnancial service transactions with the particular MFI, and 2) all other transactions. In other words,
we have examined whether the ﬁnancial service transactions are complementary to the total ofother transactions in a given month. However, there are cases where such an aggregation method
does not fully capture the Fit, or mis-states it. For example, if a loan is taken out at the end of theprevious month to pay for a large expenditure scheduled at the beginning of the next month, theexpenditure will appear in our analysis to be offset from the loan inﬂow by a month, creating theillusion of increased volatility. While there is room for research on analytic methods to reduce thepossibility of such differences, it will be di cult to eliminate them. Our next challenge is how toreconsider the issue as statistical noise and conﬁrm whether there is any directional bias and how
it can be reduced by increasing the sample size either in terms of number of diarists or time period
under consideration. Also, for better measurement of Fit, taking the noise problem into account, it
may be possible not only to investigate the frequency or combination of weekly or quarterly
aggregation but also to organize them as a transaction linking problem.

Challenges
Perhaps the most signiﬁcant challenge to the widespread use of the Fit Factor as a quantitativeand comparable impact indicator is data collection. The Hrishipara Financial Diaries used in the12analysis kept detailed records of all transactions daily for about 60 diarists. It is easy to aggregate
ﬁnancial services and other transactions and to infer the relationship between them. However, itwould be di cult for a ﬁnancial institution to conduct a similar survey of all its customers becauseof its cost.
Nevertheless, it should be reasonably simple to discover whether or not there were transactions
intended as a counterpart to a relatively large ﬁnancial service transaction, such as a loan
disbursement or a large savings withdrawal, and whether relatively small ﬁnancial transactions,such as loan repayments and frequent deposits, were carried out smoothly without puttingpressure on people's lives. This suggests there is room for research on how to approximate Fit
quantitatively and at a low cost.

Summary and Conclusion
Because it is di cult to isolate the impact of a ﬁnancial service by using measurements of what
happened after the service was used, we developed an indicator to evaluate whether the serviceﬁts the customer's life and business. We reviewed speciﬁc examples of good Fit situations, bad Fitsituations, and neither good nor bad Fit situations, respectively. Taking advantage of thequantitative deﬁnition, we grouped the results by ﬁnancial institutions and compared them.
Although some noise was observed, our analysis suggests it is nonetheless a useful indicator forcomparison across households and across ﬁnancial services and/or providers. Issues for thefuture, such as aggregation and data collection, were identiﬁed.

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